High-speed communication systems use a relatively large bandwidth to obtain data rates up to hundreds of mega bits per second. With reference to wireless and power-line systems, in such bandwidths the frequency selective nature of the channel may limit the overall system performance.
A robust modulation against the frequency-selectivity of the channel is orthogonal frequency division multiplexing (OFDM), which transforms a frequency selective channel into a set of parallel flat sub-channels. In this context, the so-called channel estimation, i.e. the estimation of functioning parameters of the transmission channel, is a crucial element to demodulate the received data. Considering packet communications, a known header is transmitted at the beginning of each packet and may be used to carry out data-aided channel estimations. The accuracy of this estimation depends on the number of symbols of the header, which is generally small to reduce the overhead of the packet.
Assuming the channel time-invariant during the transmission of several packets, a method to improve channel estimation may be to average out the estimates performed during previous packets. The phase of the estimated channel linearly depends on both the frame synchronization point and the phase of the sampling clock, hence it may be different from packet to packet. This implies that the channel estimations should not be simply averaged out, but, in order to have a reliable estimation, linear phase differences may be estimated and compensated.
This issue has been previously tackled in “Improved HomePlug AV channel estimation exploiting sounding procedure,” Riva, M. Odoni, E. Guerrini and P. Bisaglia, IEEE ISPLC 2009, pp. 296-300, 2009 and in “Improved OFDM channel estimation using inter-packet information,” D. Fu, IEEE ACSSC 2005, pp. 514-518, October 2005. Unfortunately, the technique disclosed in Riva at al. performs well when the phase of the sampling clock does not change from packet to packet, and the technique disclosed in Fu works well at high signal-to-noise ratio (SNR).